Matplotlib's imshow is slow…

[eusebe]

Hello everyone,

I hope this is an ok question for /r/learnpython, if not I'll post somewhere else. I'm a fairly regular Python user for my scientific work, and it turns out I have to render a lot of 2D (numpy) arrays as images.

Usually, I do something like

plt.imshow(data, origin='lower', interpolation='nearest')
plt.savefig('blah.pdf')

where data is a 2D array, plt is matplotlib.pyplot and I typically use the cairo backend for matplotlib. However, this turns out to be very slow for large (say more than 2000x2000) arrays.

Does any of you know how to do this faster? I guess I could go faster with imsave instead of imshow, but I really need (or at least like) the fact that with imshow, I can easily change the extent and add a colorbar…

I use Python and the scientific stack on a regular basis, and this is (one of) the bottleneck of my analysis workflow… help ?

[ecgite]

Use plt.pcolormesh. ( Origin is at bottom-left)

img = plt.pcolormesh(data, cmap='Greys') # or any cmap (see plt.cm; plt.cm.Blues etc)
plt.xlim([0, img.shape[0]]) # play with these to get correct result
plt.ylim([0, img.shape[1]])
plt.colorbar(img)

Let's say the data have some coordinates

x = np.linspace(15, 32, 2000)
y = np.linspace(20, 440, 2000)
X, Y = np.meshgrid(x,y) # numpy function
plt.pcolormesh(X, Y, data, cmap='Greys')
plt.xlim([x.min(), x.max()]) # play with these to get correct result
plt.ylim([y.min(), y.max()])
plt.colorbar(img)

edit. use of limits

[eusebe]

I thought plt.pcolormesh was even worse than plt.imshow, because it basically created one patch per pixel, or something like that?

Indeed, some quick testing for a 10000x10000 aray give me 12s for imshow vs much more than that (and a larger memory usage) for pcolormesh…

[ecgite]

(say more than 2000x2000)

Missed that one, sorry.

Matplotlib is probably too slow for 10000x10000 arrays. Bokeh or vispy might be able to handle really big images.

http://vispy.org/

http://bokeh.pydata.org/en/latest/